The R Journal: article published in 2018, volume 10:2

idmTPreg: Regression Model for Progressive Illness Death Data
Leyla Azarang and Manuel Oviedo de la Fuente , The R Journal (2018) 10:2, pages 317-325.

Abstract The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package.

Received: 2018-03-02; online 2019-02-11, supplementary material, (422 B)
CRAN packages: idmTPreg, mstate, msm, p3state.msm, doParallel, foreach, survival
CRAN Task Views cited directly: Survival
CRAN Task Views implied by cited CRAN packages: Survival, ClinicalTrials, Distributions, Econometrics, HighPerformanceComputing, SocialSciences

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-081,
  author = {Leyla Azarang and Manuel Oviedo de la Fuente},
  title = {{idmTPreg: Regression Model for Progressive Illness Death
          Data}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-081},
  url = {https://doi.org/10.32614/RJ-2018-081},
  pages = {317--325},
  volume = {10},
  number = {2}
}